Bartosz Czech - Portfolio

👋 Professional Summary

I am a Senior Bioinformatician and Product Owner with over 5 years of experience bridging the gap between computational biology and pharmaceutical R&D. Currently at 7N, I lead the development of scalable R/Shiny applications and NGS workflows for a Big Pharma client.

I hold a Ph.D. in Biological Sciences and am currently pursuing an MBA in Healthcare Management, combining deep technical expertise with strategic business insight.

---

🛠 Tech Stack

R & Shiny Python WDL & Nextflow AWS & Cloud Docker SQL & Snowflake NGS Analysis Git Agile / Scrum Product Ownership

---

🚀 Professional Experience

Senior Bioinformatician

7N / Big Pharma Client | July 2019 - Present | Warsaw, Poland

Key Responsibilities:

• Sequencing data: Next-Generation Sequencing (NGS) data analysis: scRNA-seq, bulk RNA-seq, WGS, 16S rRNA sequencing.
• Proteomics data: Analysis of mass spectrometry (MS) data for phosphoproteomics.
• R/Shiny Development: Developer of interactive dashboards for data analysis.
• Pipeline Engineering: Maintaining production-grade WDL workflows for NGS data.
• Product Ownership: Bridging scientific needs with IT solutions in an Agile environment.

Internship

Center for Quantitative Genetics and Genomics, Aarhus University | July - August 2016 | Foulum, Denmark

• Analysis of NGS data for quantitative genetics projects.

Internship

Biostat sp. z o.o. | July - August 2018 | Rybnik, Poland

• Statistical analysis of biological and medical data.

---

📚 Detailed Research Portfolio

Click on a publication card below to reveal the specific Bioinformatics & Statistics methods used.

2025Cooperation between the Hippo and MAPK pathway activation drives acquired resistance to TEAD inhibitionNature Communications▼

Bioinformatics

• High-throughput screens processed with gDR package in R.

Statistics

• Dose-response fitting using 4-parameter logistic models.

Drug Response Modeling R Synergy

Read Publication →

2024Profiling of snoRNAs in Exosomes Secreted from Cells Infected with Influenza A VirusInt. J. Mol. Sci.▼

Bioinformatics

• Small RNA-seq trimmed (Trimmomatic) and mapped with BWA-MEM.
• snoRNA annotation using Ensembl and Rfam.
• Interaction predictions queried via RNAInter and visualized with igraph/VisNetwork.

Statistics

• Differential expression via DESeq2 (Negative Binomial model).
• Statistical significance assessed with Wald test.
• Network enrichment determined by hypergeometric testing.

sRNA-seq snoRNA BWA-MEM Ensembl Rfam DESeq2

Read Publication →

2024Computational Modeling of Drug Response Identifies Mutant-Specific Constraints for Dosing panRAF and MEK InhibitorsCancers▼

Bioinformatics

• High-throughput screens processed with gDR package in R.

Statistics

• Dose-response fitting using 4-parameter logistic models.
• Synergy scores (Bliss Independence) calculated.
• Significance assessed with bootstrapped confidence intervals.

Drug Response Modeling R Synergy

Read Publication →

2023Feline sperm head morphometry in relation to male pedigree and fertilityTheriogenology▼

Bioinformatics

• Data preprocessed for multivariate analysis in R.
• K-means clustering.
• Decision tree analysis.

Statistics

• PCA and k-means clustering (factoExtra) to identify subpopulations.
• Group comparisons via ANOVA (Tukey HSD) or Kruskal–Wallis.
• Shapiro-Wilk tests for normality.

PCA Clustering ANOVA R

Read Publication →

2023Protein-Coding Region Derived Small RNA in Exosomes from Influenza A Virus-Infected CellsInt. J. Mol. Sci.▼

Bioinformatics

• Alignments to dog/flu genomes (BWA-MEM); annotation via Ensembl/Rfam.
• Functional annotation using PantherDB and STRING networks.

Statistics

• Differential expression assessed with DESeq2 (Wald test, FDR).
• Non-parametric tests (Wilcoxon) for coverage enrichment.
• Co-expression matrices for transcript analysis.

sRNA-seq Coding RNA Alignment STRING Non-parametric Tests

Read Publication →

2022Host transcriptome and microbiome interactions in Holstein cattle under heat stress conditionFrontiers in Microbiology▼

Bioinformatics

• Host RNA-seq mapped to bovine genome (STAR).
• 16S rRNA processed with QIIME2/DADA2 (SILVA taxonomy).
• Integrated analysis using WGCNA to link gene modules and taxa.

Statistics

• Differential expression/abundance via DESeq2.
• Pearson correlation for transcriptome-microbiome integration.
• Gene set enrichment with goseq/clusterProfiler.

RNA-seq Microbiome QIIME2 WGCNA Co-expression

Read Publication →

2022Fecal microbiota and their association with heat stress in Bos taurusBMC Microbiology▼

Bioinformatics

• 16S rRNA QC (Trim Galore) and denoising (DADA2/QIIME2).
• Phylogenetic tree construction.

Statistics

• Alpha/Beta diversity metrics calculated (Shannon, Bray-Curtis).
• PERMANOVA for community comparison.
• Differential abundance tested using edgeR.
• UMAP for visualization.

QIIME2 16S rRNA Metagenomics PERMANOVA edgeR

Read Publication →

2021SNP prioritization in targeted sequencing data associated with humoral immune responses in chickenPoultry Science▼

Bioinformatics

• Mapped to GRCg6a (BWA-MEM); calling with GATK HaplotypeCaller.
• Annotation via Ensembl VEP; LD analyses performed.

Statistics

• Fisher’s Exact and Chi-square tests for association.
• SNP prioritization model using allelic association and LD parameters.
• Mixed models for trait association.

GATK Variant Calling SNP Ensembl VEP Association Tests

Read Publication →

2020Extraordinary Command Line: Basic Data Editing Tools for Biologists Dealing with Sequence DataThe Open Bioinformatics Journal▼

Bioinformatics

• Demonstrated sed, awk, grep and Bash scripting.
• Workflow automation for FASTA/FASTQ/GFF files.

Statistics

N/A (Methodological Tutorial)

Linux Bash Workflow Automation Reproducibility

Read Publication →

2020The application of deep learning for the classification of correct and incorrect SNP genotypesJournal of Applied Genetics▼

Bioinformatics

• Extraction of QC features (Read Depth, MapQ, Strand Bias) from BAMs.
• Deep learning implementation with Keras/TensorFlow.

Statistics

• Model evaluation using ROC curves, AUC, and confusion matrices.
• Cross-validation for generalizability assessment.

Deep Learning Keras TensorFlow Feature Engineering Python

Read Publication →

2020Patterns of DNA variation between the autosomes, the X chromosome and the Y chromosomeScientific Reports▼

Bioinformatics

• WGS mapped (BWA-MEM), called via GATK and SAMtools.
• Annotation with snpEff and SIFT4G.

Statistics

• Diversity statistics (Tajima’s D) calculated via VCFtools.
• Kruskal-Wallis tests for chromosomal comparisons.
• Bootstrapping for CIs and Bonferroni correction.

Genomics GATK snpEff Diversity Metrics R

Read Publication →

2018Identification and annotation of breed-specific single nucleotide polymorphisms in Bos taurus genomesPLOS ONE▼

Bioinformatics

• Alignment (BWA-MEM), filtering (VCFtools, PyVCF), realignment (GATK).
• Functional analysis using KOBAS, OMIA, and QTLdb.

Statistics

• Overlap and enrichment analyses (Venn diagrams, gene sets).
• Descriptive analytics of population differentiation.

WGS PyVCF Ensembl VEP QTLdb Population Genetics

Read Publication →

---

💻 Computational Skillset Showcase

Below is an interactive gallery of 15 analytical modules demonstrating my coding expertise using realistic, simulated biological datasets. Click the tabs to explore.

🧬 Genomics

Focus: Population structure, GWAS, Variant Quality Control, and Evolution.

Principal Component Analysis (PCA)

Population structure visualization simulating realistic genotype noise.

# Parameters
n_samples <- 300
n_snps <- 1000

# Generate base matrix (noise)
G <- matrix(rnorm(n_samples * n_snps), nrow = n_samples)

# Define 3 populations with subtle shifts in allele frequencies
# Pop 1 (Rows 1-100): Shift in first 100 SNPs
G[1:100, 1:100] <- G[1:100, 1:100] + 0.5
# Pop 2 (Rows 101-200): Shift in SNPs 50-150
G[101:200, 50:150] <- G[101:200, 50:150] - 0.6
# Pop 3 (Rows 201-300): Shift in SNPs 800-900
G[201:300, 800:900] <- G[201:300, 800:900] + 0.7

groups <- c(rep("Holstein", 100), rep("Jersey", 100), rep("Angus", 100))

# Perform PCA
pca_res <- prcomp(G, scale. = TRUE)
var_explained <- round(summary(pca_res)$importance[2, 1:2] * 100, 1)
df_pca <- data.frame(PC1 = pca_res$x[,1], PC2 = pca_res$x[,2], Breed = groups)

ggplot(df_pca, aes(x = PC1, y = PC2, fill = Breed)) +
  geom_point(size = 3, shape = 21, color = "white", alpha = 0.7) +
  stat_ellipse(aes(color = Breed), geom = "polygon", alpha = 0.1, level = 0.95, show.legend = FALSE) +
  scale_fill_manual(values = c("#E69F00", "#56B4E9", "#009E73")) +
  scale_color_manual(values = c("#E69F00", "#56B4E9", "#009E73")) +
  theme_minimal() +
  labs(title = "Population Structure (PCA)",
       subtitle = "Simulated genotype matrix (300 samples x 1k SNPs)",
       x = paste0("PC1 (", var_explained[1], "% Var)"),
       y = paste0("PC2 (", var_explained[2], "% Var)"))

Manhattan Plot (GWAS)

Genome-Wide Association Study with LD-like signal peaks.

n_snps <- 5000
gwas_df <- data.frame(
  SNP = paste0("rs", 1:n_snps),
  Chr = rep(1:5, each = 1000),
  Pos = rep(1:1000, 5),
  # Background noise (uniform distribution of p-values)
  PVal = runif(n_snps)
)

# Simulate Linkage Disequilibrium (LD) Peak on Chr 2
center <- 1500
width <- 50
peak_signal <- 10^-seq(1, 8, length.out = width)
gwas_df$PVal[(center-width/2):(center+width/2-1)] <- peak_signal * runif(width, 0.1, 1)

gwas_df <- gwas_df %>% mutate(logP = -log10(PVal))

ggplot(gwas_df, aes(x = Pos, y = logP, color = as.factor(Chr))) +
  geom_point(alpha = 0.6, size = 1) +
  scale_color_brewer(palette = "Set1") +
  geom_hline(yintercept = -log10(5e-8), linetype = "dashed", color = "red", alpha = 0.5) +
  theme_minimal() +
  facet_grid(. ~ Chr, scales = "free_x", space = "free_x", switch = "x") +
  theme(axis.text.x = element_blank(), panel.grid.major.x = element_blank(), legend.position = "none") +
  labs(title = "Manhattan Plot", y = "-Log10 P-Value", x = "Chromosome")

Variant QC (Read Depth)

Quality Control: Realistic skewed distribution of Read Depth.

# Simulate sequencing depth (Negative Binomial distribution is common for read counts)
dp_values <- rnbinom(5000, size = 5, mu = 40)

df_qc <- data.frame(Depth = dp_values)

ggplot(df_qc, aes(x = Depth)) +
  geom_histogram(aes(y = ..density..), binwidth = 2, fill = "#34495e", color = "white", alpha = 0.8) +
  geom_density(color = "#e74c3c", size = 1, adjust = 2) +
  scale_x_continuous(limits = c(0, 150)) +
  theme_light() +
  labs(title = "Variant Quality Control (DP)", 
       subtitle = "Distribution of sequencing depth across variants",
       x = "Read Depth (DP)", y = "Density") +
  geom_vline(xintercept = 40, linetype="dashed", color="orange") +
  annotate("text", x = 60, y = 0.02, label = "Mean Depth ~ 40x", color = "orange")

Phylogenetic Tree

Hierarchical clustering of genetic distances.

# Simulate distance matrix with some structure
m <- matrix(rnorm(100), nrow = 10)
rownames(m) <- paste0("Species_", LETTERS[1:10])
dist_mat <- dist(m)
hc <- hclust(dist_mat, method = "ward.D2")

par(mar=c(2,2,2,2))
plot(hc, hang = -1, main = "Phylogenetic Tree Reconstruction", 
     sub = "Ward's method clustering", xlab = "",
     col = "#2980b9", lwd = 2)
rect.hclust(hc, k = 3, border = "red") # Highlight 3 clades

🔬 Transcriptomics

Focus: Expression profiling, Pathways, and Single-Cell.

Volcano Plot

Differential Expression: Realistic relationship between Fold Change and P-value.

n_genes <- 3000
# Generate LogFC: mostly close to 0, heavy tails
logfc <- c(rnorm(2500, 0, 0.5), rnorm(250, 2, 1), rnorm(250, -2, 1))
# Generate P-values dependent on LogFC (stronger effect -> smaller p-value)
noise <- rnorm(n_genes, 0, 2)
log_pval <- -(abs(logfc) * 3) + noise 
pvals <- 10^log_pval
pvals[pvals > 1] <- 1

df_vol <- data.frame(
  Gene = paste0("G", 1:n_genes),
  log2FoldChange = logfc,
  padj = p.adjust(pvals, method = "BH")
) %>%
  mutate(
    Group = case_when(
      log2FoldChange > 1 & padj < 0.05 ~ "Up-regulated",
      log2FoldChange < -1 & padj < 0.05 ~ "Down-regulated",
      TRUE ~ "NS"
    )
  )

ggplot(df_vol, aes(x = log2FoldChange, y = -log10(padj), color = Group)) +
  geom_point(alpha = 0.5, size = 1.5) +
  scale_color_manual(values = c("steelblue", "grey85", "firebrick")) +
  geom_hline(yintercept = -log10(0.05), linetype = "dashed", alpha = 0.5) +
  geom_vline(xintercept = c(-1, 1), linetype = "dashed", alpha = 0.5) +
  theme_minimal() +
  labs(title = "Differential Expression (Volcano Plot)", 
       subtitle = "Simulated DESeq2 output with FDR correction",
       x = "Log2 Fold Change", y = "-Log10 Adjusted P-value")

Single-Cell UMAP

Simulating cellular trajectories/clusters.

# Create a "trajectory" shape rather than just blobs
t <- seq(0, 2*pi, length.out = 300)
x <- c(cos(t), cos(t) + 2.5, cos(t) + 1.2) + rnorm(900, 0, 0.2)
y <- c(sin(t), sin(t) + 1, sin(t) - 2) + rnorm(900, 0, 0.2)
clusters <- c(rep("Stem", 300), rep("Progenitor", 300), rep("Differentiated", 300))

df_umap <- data.frame(UMAP1 = x, UMAP2 = y, CellType = clusters)

ggplot(df_umap, aes(UMAP1, UMAP2, color = CellType)) +
  geom_point(size = 0.8, alpha = 0.6) +
  scale_color_manual(values = c("#9b59b6", "#3498db", "#e74c3c")) +
  theme_void() +
  theme(legend.position = "right") +
  labs(title = "Single-Cell UMAP Projection", subtitle = "Developmental Trajectory Simulation")

Pathway Enrichment

Dotplot for GSEA results.

df_path <- data.frame(
  Pathway = c("Cell Cycle", "DNA Replication", "P53 Signaling", "Apoptosis", "MAPK Signaling", "Ribosome"),
  Count = c(45, 30, 25, 20, 15, 10),
  GeneRatio = c(0.15, 0.12, 0.09, 0.08, 0.05, 0.03),
  p.adjust = c(1e-9, 1e-6, 0.001, 0.01, 0.03, 0.045)
)
df_path$Pathway <- factor(df_path$Pathway, levels = df_path$Pathway[order(df_path$GeneRatio)])

ggplot(df_path, aes(x = GeneRatio, y = Pathway)) +
  geom_point(aes(size = Count, color = p.adjust)) +
  scale_color_gradient(low = "red", high = "blue") +
  theme_light() +
  labs(title = "KEGG Pathway Enrichment", x = "Gene Ratio", y = "")

Heatmap

Visualizing co-expression modules.

# Create structured matrix
mat <- matrix(rnorm(400), nrow = 20, ncol = 20)
# Add "modules" of correlation
mat[1:10, 1:10] <- mat[1:10, 1:10] + 2
mat[11:20, 11:20] <- mat[11:20, 11:20] - 2

df_h <- as.data.frame(mat)
colnames(df_h) <- paste0("S", 1:20)
df_h$Gene <- paste0("G", 1:20)
df_h_long <- pivot_longer(df_h, cols = -Gene, names_to = "Sample", values_to = "Exp")

ggplot(df_h_long, aes(Sample, Gene, fill = Exp)) +
  geom_tile() +
  scale_fill_gradient2(low = "#2c7bb6", mid = "#ffffbf", high = "#d7191c") +
  theme_minimal() +
  theme(axis.text.x = element_blank()) +
  labs(title = "Gene Expression Heatmap", x = "Samples (n=20)", y = "Genes (n=20)")

🏥 Clinical & Stats

Focus: Survival, Longitudinal Models, and Correlations.

Survival Analysis (Kaplan-Meier)

Time-to-event analysis with realistic censoring.

n <- 100
# Generate Weibull distributed times (more realistic for biological failure)
T_treat <- rweibull(n, shape = 1.5, scale = 100)
T_placebo <- rweibull(n, shape = 1.5, scale = 60) # Placebo dies faster

# Combine
df_surv <- data.frame(
  Time = c(T_treat, T_placebo),
  Group = rep(c("Treatment", "Placebo"), each = n)
)

# Add random censoring (patients leaving study)
cens_time <- runif(2*n, 0, 150)
df_surv$Status <- ifelse(df_surv$Time < cens_time, 1, 0) # 1=Event, 0=Censored
df_surv$TimeObs <- pmin(df_surv$Time, cens_time)

fit <- survfit(Surv(TimeObs, Status) ~ Group, data = df_surv)

# Plot
plot(fit, col=c("red", "blue"), lwd=3, xlab="Time (Months)", ylab="Survival Probability",
     main="Kaplan-Meier Analysis (Simulated Clinical Trial)", frame.plot=FALSE)
legend("topright", levels(factor(df_surv$Group)), col=c("red", "blue"), lwd=3, bty="n")
grid(col="grey90")

Linear Mixed Models (LMM)

Longitudinal data with realistic subject variance.

subjects <- 20
times <- 0:5
# Random intercepts and slopes
intercepts <- rnorm(subjects, 10, 2)
slopes <- rnorm(subjects, 0.5, 0.2)

df_lmm <- data.frame()
for(i in 1:subjects){
  # Add group effect: Group B increases faster
  grp <- ifelse(i > 10, "B", "A")
  slope_eff <- slopes[i] + ifelse(grp=="B", 1.5, 0)
  
  y <- intercepts[i] + slope_eff * times + rnorm(length(times), 0, 1)
  df_lmm <- rbind(df_lmm, data.frame(ID=paste0("S",i), Time=times, Value=y, Group=grp))
}

ggplot(df_lmm, aes(x=Time, y=Value, group=ID, color=Group)) +
  geom_line(alpha=0.4) +
  stat_summary(aes(group=Group), fun=mean, geom="line", size=2) +
  scale_color_manual(values=c("#95a5a6", "#e74c3c")) +
  theme_bw() +
  labs(title="Longitudinal Response (LMM)", subtitle="Group A vs B treatment trajectories")

Correlation Matrix

Clinical variable relationships.

# Simulate correlated multivariate data
sigma <- matrix(c(1, 0.8, -0.5, 0.8, 1, -0.3, -0.5, -0.3, 1), 3, 3)
data_corr <- mvrnorm(n = 50, mu = c(0,0,0), Sigma = sigma)
colnames(data_corr) <- c("BMI", "Insulin", "Activity")
cormat <- round(cor(data_corr), 2)
melted <- as.data.frame(as.table(cormat))

ggplot(melted, aes(Var1, Var2, fill=Freq)) + 
  geom_tile(color="white") + 
  geom_text(aes(label=Freq)) +
  scale_fill_gradient2(low="#2980b9", mid="white", high="#c0392b") + 
  theme_minimal() + labs(title="Clinical Correlations", x="", y="")

🦠 Microbiome

Focus: Community Diversity and Composition.

Alpha Diversity

Comparing species richness (Non-normal distributions).

# Generate Gamma distributed data (common for counts/diversity)
grp_a <- rgamma(40, shape=20, scale=0.2)
grp_b <- rgamma(40, shape=15, scale=0.2)

df_alpha <- data.frame(Index = c(grp_a, grp_b), Group = rep(c("Healthy", "Dysbiosis"), each=40))

ggplot(df_alpha, aes(x=Group, y=Index, fill=Group)) + 
  geom_violin(alpha=0.3, trim=FALSE) +
  geom_boxplot(width=0.2, alpha=0.8) +
  scale_fill_manual(values=c("#e74c3c", "#2ecc71")) + 
  theme_classic() + 
  labs(title="Microbiome Alpha Diversity (Shannon)", y="Diversity Index")

Beta Diversity (PCoA)

Visualizing community separation.

# Create two multivariate normal distributions
g1 <- mvrnorm(25, mu=c(2, 2), Sigma=matrix(c(1,0.5,0.5,1),2))
g2 <- mvrnorm(25, mu=c(-1, -1), Sigma=matrix(c(1,-0.2,-0.2,1),2))
df_beta <- rbind(data.frame(g1, Grp="Ctrl"), data.frame(g2, Grp="Treat"))
colnames(df_beta)[1:2] <- c("PCoA1", "PCoA2")

ggplot(df_beta, aes(PCoA1, PCoA2, color=Grp)) + 
  geom_point(size=3) + 
  stat_ellipse() + 
  theme_minimal() + 
  labs(title="Beta Diversity (Bray-Curtis PCoA)", subtitle="Clustering of microbial communities")

🤖 Pharma & ML

Focus: Drug Discovery and Machine Learning.

Dose-Response (S-Curve)

Pharmacodynamics using 4-Parameter Logistic Model.

# Define 4-Parameter Logistic Function
f_4pl <- function(x, b, c, d, e) { c + (d - c) / (1 + exp(b * (log(x) - log(e)))) }

concs <- c(0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000)
# Parameters: b=Slope, c=Bottom, d=Top, e=IC50
true_y <- f_4pl(concs, b = -1.5, c = 5, d = 100, e = 50) 

# Add biological replicates and noise
df_dr <- data.frame(
  Conc = rep(concs, each=3)
)
df_dr$Resp <- rep(true_y, each=3) + rnorm(nrow(df_dr), 0, 5) # Noise

ggplot(df_dr, aes(x=Conc, y=Resp)) + 
  geom_point(size=2.5, alpha=0.6, color="#8e44ad") + 
  geom_function(fun = function(x) f_4pl(x, -1.5, 5, 100, 50), color="#2c3e50", size=1) +
  scale_x_log10() + 
  theme_bw() + 
  labs(title="Drug Dose-Response (Sigmoidal)", 
       subtitle="IC50 = 50 nM (4-Parameter Logistic Fit)",
       x="Concentration (nM) [Log]", y="Cell Viability (%)") +
  geom_vline(xintercept=50, linetype="dashed", color="red")

ROC Curve (Classification)

Model performance metrics.

# Simulate scores for Positive and Negative classes
neg_scores <- rnorm(500, mean=0.3, sd=0.15)
pos_scores <- rnorm(500, mean=0.7, sd=0.15) # Better separation

# Calculate ROC points manually
thresholds <- seq(0, 1, 0.01)
tpr <- sapply(thresholds, function(t) mean(pos_scores > t))
fpr <- sapply(thresholds, function(t) mean(neg_scores > t))

df_roc <- data.frame(FPR=fpr, TPR=tpr)

ggplot(df_roc, aes(x=FPR, y=TPR)) + 
  geom_path(color="#27ae60", size=1.5) +
  geom_abline(linetype="dashed", color="grey") +
  theme_light() + 
  annotate("text", x=0.75, y=0.25, label="AUC = 0.96", size=6, color="#27ae60") +
  labs(title="ROC Analysis: SNP Classifier", x="1 - Specificity (FPR)", y="Sensitivity (TPR)")

🎓 Education

• MBA in Healthcare Management | Poznan Univ. of Medical Sciences (2024-Current)
• Ph.D. in Biological Sciences | Wroclaw Univ. of Env. and Life Sciences (2019-2024)
• M.Sc. in Bioinformatics | Wroclaw Univ. of Env. and Life Sciences (2017-2019)

---

Contact

📧 bartosz.w.czech@gmail.com